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Found 18 papers, 6 papers with code
END: Early Noise Dropping for Efficient and Effective Context Denoising
Large Language Models (LLMs) have demonstrated remarkable performance across a wide range of natural language processing tasks.
Hephaestus: Improving Fundamental Agent Capabilities of Large Language Models through Continual Pre-Training
Due to the scarcity of agent-oriented pre-training data, LLM-based autonomous agents typically rely on complex prompting or extensive fine-tuning, which often fails to introduce new capabilities while preserving strong generalizability.
Scaling Laws for Predicting Downstream Performance in LLMs
Our two-stage approach consists of first estimating a function that maps computational resources (e. g., FLOPs) to the pre-training Loss using a series of sampling models, followed by mapping the pre-training loss to downstream task Performance after the critical "emergent phase".
Inductive or Deductive? Rethinking the Fundamental Reasoning Abilities of LLMs
To investigate into the true inductive reasoning capabilities of LLMs, we propose a novel framework, SolverLearner.
Concept2Box: Joint Geometric Embeddings for Learning Two-View Knowledge Graphs
We propose Concept2Box, a novel approach that jointly embeds the two views of a KG using dual geometric representations.
Label-Efficient Self-Training for Attribute Extraction from Semi-Structured Web Documents
Prior works rely on a few human-labeled web pages from each target website or thousands of human-labeled web pages from some seed websites to train a transferable extraction model that generalizes on unseen target websites.
DOM-LM: Learning Generalizable Representations for HTML Documents
We argue that the text and HTML structure together convey important semantics of the content and therefore warrant a special treatment for their representation learning.
Ranked #2 on
Attribute Extraction
on SWDE
RATE: Overcoming Noise and Sparsity of Textual Features in Real-Time Location Estimation
Real-time location inference of social media users is the fundamental of some spatial applications such as localized search and event detection.
TCN: Table Convolutional Network for Web Table Interpretation
Existing work linearize table cells and heavily rely on modifying deep language models such as BERT which only captures related cells information in the same table.
Disinformation and Misinformation on Twitter during the Novel Coronavirus Outbreak
Tweets mentioning "fake news" URLs and disinformation story-lines are also more likely to be spread by regular users.
Social and Information Networks
Discover Your Social Identity from What You Tweet: a Content Based Approach
We further propose a transfer learning scheme that improves our model's performance by a large margin.
A Hierarchical Location Prediction Neural Network for Twitter User Geolocation
In this paper, we propose a hierarchical location prediction neural network for Twitter user geolocation.
Parameterized Convolutional Neural Networks for Aspect Level Sentiment Classification
Using parameterized filters and parameterized gates, we incorporate aspect information into convolutional neural networks (CNN).
Syntax-Aware Aspect Level Sentiment Classification with Graph Attention Networks
Aspect level sentiment classification aims to identify the sentiment expressed towards an aspect given a context sentence.
Recurrent U-net: Deep learning to predict daily summertime ozone in the United States
We used the model to evaluate recent trends in NO$_x$ emissions in the US and found that the trend in the EPA emission inventory produced the largest negative bias in MDA8 ozone between 2010-2016.
Residual or Gate? Towards Deeper Graph Neural Networks for Inductive Graph Representation Learning
In this paper, we study the problem of node representation learning with graph neural networks.
Aspect Level Sentiment Classification with Attention-over-Attention Neural Networks
Aspect Based Sentiment Analysis, PyTorch Implementations.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+3
A Probabilistic Framework for Location Inference from Social Media
In this paper, we formalize the problem of inferring location from social media into a semi-supervised factor graph model (SSFGM).
